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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1276
DC FieldValueLanguage
dc.contributor.authorHuynh Duc Khaien_US
dc.date.accessioned2022-01-18T04:29:31Z-
dc.date.available2022-01-18T04:29:31Z-
dc.date.issued2021-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/1276-
dc.description.abstractThis thesis will introduce the core idea for making software that is able to detect whether a person has a qualified hand washing process or not by taking advantage of modern technology: Deep Learning. More specifically, by applying a new variation of Recurrent Neural Network (RNN) - the Liquid Time-constant (LTC) Network - the software now can have more accurate in classifying an entire handwashing process compared to vanilla RNN algorithm of its - the famous Long Short Term Memory (LSTM) networks. In this paper, I take one step further by creating multiple LTC networks, combine them into a decision forest and add one more layer outside to determine which recorded video is a qualified hand washing process. Although LTC improves effectiveness, we still need more consideration to compare it to more recent advanced variations of neuron networks to understand more about its strength and weakness.en_US
dc.language.isoenen_US
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectLiquid time-constanten_US
dc.subjectHandwashing processen_US
dc.subjectRecurrent neural networken_US
dc.subjectDeep learning applicationen_US
dc.titleThe application of liquid time-constant networks in learning handwashing processen_US
dc.typeThesisen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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